{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading Cal. housing from http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz to C:\\Users\\user\\scikit_learn_data\n",
      "California housing dataset.\n",
      "\n",
      "The original database is available from StatLib\n",
      "\n",
      "    http://lib.stat.cmu.edu/\n",
      "\n",
      "The data contains 20,640 observations on 9 variables.\n",
      "\n",
      "This dataset contains the average house value as target variable\n",
      "and the following input variables (features): average income,\n",
      "housing average age, average rooms, average bedrooms, population,\n",
      "average occupation, latitude, and longitude in that order.\n",
      "\n",
      "References\n",
      "----------\n",
      "\n",
      "Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "Statistics and Probability Letters, 33 (1997) 291-297.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets.california_housing import fetch_california_housing\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   8.3252    ,   41.        ,    6.98412698,    1.02380952,\n",
       "        322.        ,    2.55555556,   37.88      , -122.23      ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "dtr = tree.DecisionTreeRegressor(max_depth = 2)\n",
    "dtr.fit(housing.data[:, [6, 7]], housing.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#要可视化显示 首先需要安装 graphviz   http://www.graphviz.org/Download..php\n",
    "dot_data = \\\n",
    "    tree.export_graphviz(\n",
    "        dtr,\n",
    "        out_file = None,\n",
    "        feature_names = housing.feature_names[6:8],\n",
    "        filled = True,\n",
    "        impurity = False,\n",
    "        rounded = True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAErCAYAAABn+c0UAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nBMIrylsAVqnbCAKBQCAQCAQCgdCBX/8LS7ZBQFZjEAIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pip install pydotplus\n",
    "import pydotplus\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.get_nodes()[7].set_fillcolor(\"#FFF2DD\")\n",
    "from IPython.display import Image\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.write_png(\"dtr_white_background.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.637318351331017"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "data_train, data_test, target_train, target_test = \\\n",
    "    train_test_split(housing.data, housing.target, test_size = 0.1, random_state = 42)\n",
    "dtr = tree.DecisionTreeRegressor(random_state = 42)\n",
    "dtr.fit(data_train, target_train)\n",
    "\n",
    "dtr.score(data_test, target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79086492280964926"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor( random_state = 42)\n",
    "rfr.fit(data_train, target_train)\n",
    "rfr.score(data_test, target_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 树模型参数:\n",
    "\n",
    "-  1.criterion  gini  or  entropy\n",
    "\n",
    "-  2.splitter  best or random 前者是在所有特征中找最好的切分点 后者是在部分特征中（数据量大的时候）\n",
    "\n",
    "-  3.max_features  None（所有），log2，sqrt，N  特征小于50的时候一般使用所有的\n",
    "\n",
    "-  4.max_depth  数据少或者特征少的时候可以不管这个值，如果模型样本量多，特征也多的情况下，可以尝试限制下\n",
    "\n",
    "-  5.min_samples_split  如果某节点的样本数少于min_samples_split，则不会继续再尝试选择最优特征来进行划分如果样本量不大，不需要管这个值。如果样本量数量级非常大，则推荐增大这个值。\n",
    "\n",
    "-  6.min_samples_leaf  这个值限制了叶子节点最少的样本数，如果某叶子节点数目小于样本数，则会和兄弟节点一起被剪枝，如果样本量不大，不需要管这个值，大些如10W可是尝试下5\n",
    "\n",
    "-  7.min_weight_fraction_leaf 这个值限制了叶子节点所有样本权重和的最小值，如果小于这个值，则会和兄弟节点一起被剪枝默认是0，就是不考虑权重问题。一般来说，如果我们有较多样本有缺失值，或者分类树样本的分布类别偏差很大，就会引入样本权重，这时我们就要注意这个值了。\n",
    "\n",
    "-  8.max_leaf_nodes 通过限制最大叶子节点数，可以防止过拟合，默认是\"None”，即不限制最大的叶子节点数。如果加了限制，算法会建立在最大叶子节点数内最优的决策树。如果特征不多，可以不考虑这个值，但是如果特征分成多的话，可以加以限制具体的值可以通过交叉验证得到。\n",
    "\n",
    "-  9.class_weight 指定样本各类别的的权重，主要是为了防止训练集某些类别的样本过多导致训练的决策树过于偏向这些类别。这里可以自己指定各个样本的权重如果使用“balanced”，则算法会自己计算权重，样本量少的类别所对应的样本权重会高。\n",
    "\n",
    "- 10.min_impurity_split 这个值限制了决策树的增长，如果某节点的不纯度(基尼系数，信息增益，均方差，绝对差)小于这个阈值则该节点不再生成子节点。即为叶子节点 。\n",
    "- n_estimators:要建立树的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.78405, std: 0.00505, params: {'min_samples_split': 3, 'n_estimators': 10},\n",
       "  mean: 0.80529, std: 0.00448, params: {'min_samples_split': 3, 'n_estimators': 50},\n",
       "  mean: 0.80673, std: 0.00433, params: {'min_samples_split': 3, 'n_estimators': 100},\n",
       "  mean: 0.79016, std: 0.00124, params: {'min_samples_split': 6, 'n_estimators': 10},\n",
       "  mean: 0.80496, std: 0.00491, params: {'min_samples_split': 6, 'n_estimators': 50},\n",
       "  mean: 0.80671, std: 0.00408, params: {'min_samples_split': 6, 'n_estimators': 100},\n",
       "  mean: 0.78747, std: 0.00341, params: {'min_samples_split': 9, 'n_estimators': 10},\n",
       "  mean: 0.80481, std: 0.00322, params: {'min_samples_split': 9, 'n_estimators': 50},\n",
       "  mean: 0.80603, std: 0.00437, params: {'min_samples_split': 9, 'n_estimators': 100}],\n",
       " {'min_samples_split': 3, 'n_estimators': 100},\n",
       " 0.8067250881273065)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "tree_param_grid = { 'min_samples_split': list((3,6,9)),'n_estimators':list((10,50,100))}\n",
    "grid = GridSearchCV(RandomForestRegressor(),param_grid=tree_param_grid, cv=5)\n",
    "grid.fit(data_train, target_train)\n",
    "grid.grid_scores_, grid.best_params_, grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80908290496531576"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor( min_samples_split=3,n_estimators = 100,random_state = 42)\n",
    "rfr.fit(data_train, target_train)\n",
    "rfr.score(data_test, target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0.524257\n",
       "AveOccup      0.137947\n",
       "Latitude      0.090622\n",
       "Longitude     0.089414\n",
       "HouseAge      0.053970\n",
       "AveRooms      0.044443\n",
       "Population    0.030263\n",
       "AveBedrms     0.029084\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(rfr.feature_importances_, index = housing.feature_names).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
